Hidden process models

We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t ]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Rebecca Hutchinson, Tom M. Mitchell, Indrayana Rustandi
• Keywords: Dynamic Bayes Networks, Hidden Cognitive Processes, Hidden Process Models, ICML 2006, Machine Learning